Known control systems typically are feed-forward or feedback systems, which implement closed-loop control to obtain and maintain certain operating conditions. Conventionally, control systems are often used to maintain a prescribed controlled system operating state or condition such as temperature, speed, position, trajectory, torque, etc., or to achieve a prescribed system state such as for motion control and robotics applications. These systems typically implement a stable control law that works to maintain operating performance notwithstanding certain operational and/or physical constraints and extekal disturbances. Moreover, many of these systems exhibit nonlinear characteristics. For example, servo actuator systems have inherent non-linear characteristics. Control design typically requires that a nonlinear system be treated as linear, usually by canceling the nonlinearity using feedback. These “linearizing” techniques have not found wide acceptance because they are generally not robust enough to operate in real-world applications. Often they are infeasible due to the large number of control inputs required to regulate the system. Consequently, these techniques have had relatively little use in solving real-world problems. For instance, the non-linearities associated with the aforementioned servo actuator systems cannot be ignored in control design. As such, a control system is needed that accounts for system nonlinearities using an alternative to correcting for such nonlinearlities with “linearizing” techniques.
Some known control systems utilize non-linear control methods such as model-reference (MRAC), gain scheduling, controller scheduling, fuzzy logic, or feedback linearization, to dynamically modify the operation of the controller in response to sensed changes in system behavior. Such changes in behavior include different system dynamics including compliance, noise or other related changes. Moreover, such systems may be time varying and may be difficult or impossible to model for control purposes. Notably, these control systems typically operate in isolation from any diagnostic or prognostic systems.
In addition to providing control, some systems implement independent diagnostics apparatus to monitor the overall health of either the apparatus being controlled, or the control system itself. Some systems may have no control, but only machinery diagnostics capabilities. Notably, assessing system health can be used to minimize unscheduled system downtime and to prevent equipment failure. This capability can avoid a potentially dangerous situation caused by the unexpected outage or catastrophic failure of machinery. Moreover, some diagnostic systems inconveniently require an operator to manually collect data from machinery using portable, hand-held data acquisition probes.
Other known systems have sensors and data acquisition and network equipment permanently attached to critical machinery for remote diagnostics. Typically the diagnostics equipment is directed to detecting problems with the control system hardware itself or monitoring the integrity of the output, i.e., monitoring when the control system response is outside prescribed time or value limits. As noted above, control system health monitoring, health assessment and prognostics generally are performed in isolation from any associated control system. These systems typically conduct passive monitoring and assess system health using diagnostic algorithms and sensors dedicated to establish system health. This passive monitoring is frequently done using off-line, batch-mode data acquisition and analysis to establish the health of the system.
For example, in FIG. 1, a conventional prior art automated control and diagnostics monitoring system 10 for use with a machine 12 that operates a plant (or as part of a process) is shown. System 10 includes a control module 14 that provides closed loop feedback control of machine 12 to maintain a set point condition (e.g., a velocity). In addition, system 10 includes a diagnostics block 16 electrically coupled to machine 12 for monitoring the health of the machine. In particular, diagnostics block 16 receives sampled systems data and processes the data to assess the health of the machine 12. A primary drawback of such a system is that diagnostics block 16 operates independent and isolated from control module 14 and performs off-line diagnostic processing which is not readily adaptable to integration with on-line control.
However, as noted previously, because virtually all diagnostics systems perform off-line diagnostic processing, it has been extremely difficult to implement diagnostics processing real-time in coordination with on-line control. Presently, no system exists which integrates control and diagnostics to optimize control outputs dynamically in real-time.
As a result, the art of control and diagnostics systems is in need of a control and diagnostics system that advantageously utilizes the outputs of each system to optimize the performance of both systems. Such a system would be able to dynamically optimize the operation of the controlled system by accurately diagnosing problems and predicting the future state of the controlled system based on health data from diagnostic sensors and/or from the control system. This would enable the system to alter the control operation in a goal-directed manner to facilitate diagnostics and prognostics, to reduce or eliminate excessive wear or degradation of the controlled system, or to achieve other operational objectives.